Load data and libraries

##################
# LOAD LIBRARIES #
##################
library(tidyverse)
library(Seurat)
library(tidyseurat)
library(cowplot)
library(harmony)
source("../bin/plotting_functions.R")

#########
# PATHS #
#########
input_dir <- "../results/01_QC_st_data/"
result_dir <- "../results/02_integrate_st_data/"
if( isFALSE(dir.exists(result_dir)) ) { dir.create(result_dir,recursive = TRUE) }

#############
# LODA DATA #
#############
DATA <- readRDS(paste0(input_dir,"seuratObj_filtered.RDS"))

Identify Highly Variable Genes (HVG) across samples

################################
# SPLIT INTO SEPERATE DATASETS #
################################
DATA_nested <- DATA %>%
  mutate(batch = orig.ident) %>%
  nest(data = -batch) %>%
  mutate(data = imap(
    data, ~ .x %>%
      NormalizeData(., normalization.method = "LogNormalize", 
                    verbose = FALSE) %>%
      FindVariableFeatures(selection.method = "vst", 
                           nfeatures = 2000, 
                           verbose = FALSE) )) %>%
  mutate(data = setNames(.[["data"]], .$batch))

#########################################
# FIND HIGLY VARIABLE GENES PER DATASET #
#########################################
hvgs_heat <- DATA_nested %>%
  .$data %>%
  map(., ~ .x@assays$RNA@var.features) %>%
  ( function(x){unique(unlist(x)) ->> hvgs_all; return(x)} ) %>%
  # intersect across all samples:
  ( function(x){Reduce(intersect, x) ->> hvgs; return(x)} ) %>% 
  imap_dfc(., ~hvgs_all %in% .x, .id=.y) %>%
  mutate(rownames = hvgs_all) %>%
  column_to_rownames(var = "rownames")

# choose the hvg present in at least two samples:
hig_var <- rownames(hvgs_heat)[rowSums(hvgs_heat)>2]

# remove all VDJ-genes from list of HVG
remove <- str_subset(hig_var, "^IGH|^IGK|^IGL|^TRA|^TRB|^TRD|^TRG")
hig_var <- setdiff(hig_var, remove)

Heatmap of HVG in all samples

pheatmap::pheatmap(t(hvgs_heat * 1), cluster_rows = F, color = c("grey90", "grey20"))

Integration

############
# HARMONY #
###########
DATA <- DATA %>%
  SCTransform(verbose = FALSE) %>%
  FindVariableFeatures(selection.method = "vst",
                      nfeatures = 4000,
                      verbose = FALSE) %>%
  ScaleData(verbose = FALSE, features = hig_var ) %>%
  RunPCA(verbose = FALSE, npcs = 50) %>%
  RunUMAP(dims = 1:50,
          n.components = 2L,
          n.neighbors = 10,
          min.dist = .1,
          spread = .3) 

DATA <- DATA %>%
  RunHarmony(group.by.vars = "orig.ident", 
             reduction.use = "pca",
             dims.use = 1:50, 
             assay.use = "RNA") #%>%

DATA <-   DATA %>%
  RunUMAP(dims = 1:50, 
          n.neighbors = 10,
          min.dist = .1,
          spread = 1,
          repulsion.strength = 1,
          negative.sample.rate = 10,
          n.epochs = 100,
          reduction = "harmony",
          reduction.name = "umapharmony")

Alternative graph based UMAP

integrated <- DATA@reductions$harmony@cell.embeddings
ann <- RcppHNSW::hnsw_build(as.matrix(integrated), distance = "cosine")
knn <- RcppHNSW::hnsw_search(as.matrix(integrated) , ann = ann , k = 15)

UU2 <- uwot::umap(X = NULL,
                 nn_method =  knn,
                 n_components = 2,
                 ret_extra = c("model","fgraph"),
                 verbose = T,
                 min_dist = 0.1,
                 spread = .3,
                 repulsion_strength = 1,
                 negative_sample_rate = 10,
                 n_epochs = 150,
                 n_threads = 8)
dimnames(UU2$embedding) <- list(colnames(DATA),paste0("umap_harmony_knn_", 1:2))
DATA@reductions[["umap_harmony_knn"]] <- CreateDimReducObject(embeddings = UU2$embedding, 
                                                              key = "umap_harmony_knn_")
colnames(DATA@reductions$umap_harmony_knn@cell.embeddings) <- paste0("umap_harmony_knn_", 1:2)
res <- c("umapharmony", "umap_harmony_knn")
p <- map(res, ~plot_clusters.fun(DATA, red=.x, cluster="orig.ident", lable=FALSE, txt_size = 7))
plot_grid(ncol = 2, 
          plotlist = p)

Plot before and after integration

#  dev.new(height=6, width=6.6929133858, noRStudioGD = TRUE)
res <- c("PC", "harmony", "UMAP", "umapharmony")
title <- c("PCA raw data", "PCA Harmony integrated", "UMAP raw data", "UMAP Harmony integrated")
p <- map2(res, title, 
          ~plot_clusters.fun(DATA, 
                             cluster="orig.ident", txt_size = 9,
                             red=.x, lable=FALSE, title=.y))
plot_grid(ncol = 2, 
         plotlist = p)

Plot marker genes

#  dev.new(height=3, width=8, noRStudioGD = TRUE)
################################
# VISUALIZE EXPR. OF KEY GENES #
################################
# col <- c("grey90","grey80","grey60","navy","black")
col <- c("#FFF5F0", "#FEE0D2", "#FCBBA1", "#FC9272", "#FB6A4A", "#EF3B2C", "#CB181D", "#A50F15", "#67000D")
genes <- c("KRT1", "KRT15", "CDH1")
# genes <- c("CD8A", "SFRP2", "CD3E")
# genes <- c("CD8A", "MYOZ2", "CD3E", "EPCAM", "COL6A1", "CD4")

p <- map(genes, ~plot_genes.fun(DATA, .x, col = col, lable = FALSE, red="umapharmony"))
plot_grid(ncol = 3, 
          plotlist = p)

Save seurat object

##################################
# SAVE INTERMEDIATE SEURAT OJECT #
##################################
saveRDS(DATA, paste0(result_dir,"seuratObj_integrated_SCTnorm.RDS"))
# DATA <- readRDS(paste0(result_dir,"seuratObj_integrated.RDS"))

Session info

sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-apple-darwin20
## Running under: macOS Sonoma 14.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Stockholm
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] harmony_1.2.0      Rcpp_1.0.12        cowplot_1.1.3      tidyseurat_0.8.0  
##  [5] ttservice_0.4.0    SeuratObject_5.0.2 Seurat_4.4.0       lubridate_1.9.3   
##  [9] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.2       
## [13] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.1     
## [17] tidyverse_2.0.0   
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3     rstudioapi_0.16.0      jsonlite_1.8.8        
##   [4] magrittr_2.0.3         spatstat.utils_3.0-4   farver_2.1.2          
##   [7] rmarkdown_2.27         fs_1.6.4               vctrs_0.6.5           
##  [10] ROCR_1.0-11            spatstat.explore_3.2-7 htmltools_0.5.8.1     
##  [13] sass_0.4.9             sctransform_0.4.1      parallelly_1.37.1     
##  [16] KernSmooth_2.23-24     bslib_0.7.0            htmlwidgets_1.6.4     
##  [19] ica_1.0-3              plyr_1.8.9             plotly_4.10.4         
##  [22] zoo_1.8-12             cachem_1.1.0           igraph_2.0.3          
##  [25] mime_0.12              lifecycle_1.0.4        pkgconfig_2.0.3       
##  [28] Matrix_1.7-0           R6_2.5.1               fastmap_1.2.0         
##  [31] fitdistrplus_1.1-11    future_1.33.2          shiny_1.8.1.1         
##  [34] digest_0.6.35          colorspace_2.1-1       patchwork_1.2.0       
##  [37] tensor_1.5             irlba_2.3.5.1          labeling_0.4.3        
##  [40] progressr_0.14.0       fansi_1.0.6            spatstat.sparse_3.0-3 
##  [43] timechange_0.3.0       httr_1.4.7             polyclip_1.10-6       
##  [46] abind_1.4-5            compiler_4.4.1         withr_3.0.0           
##  [49] highr_0.10             MASS_7.3-60.2          tools_4.4.1           
##  [52] lmtest_0.9-40          httpuv_1.6.15          future.apply_1.11.2   
##  [55] goftest_1.2-3          glue_1.7.0             nlme_3.1-164          
##  [58] promises_1.3.0         grid_4.4.1             Rtsne_0.17            
##  [61] cluster_2.1.6          reshape2_1.4.4         generics_0.1.3        
##  [64] gtable_0.3.5           spatstat.data_3.0-4    tzdb_0.4.0            
##  [67] data.table_1.15.4      hms_1.1.3              sp_2.1-4              
##  [70] utf8_1.2.4             spatstat.geom_3.2-9    RcppAnnoy_0.0.22      
##  [73] ggrepel_0.9.5          RANN_2.6.1             pillar_1.9.0          
##  [76] RcppHNSW_0.6.0         spam_2.10-0            later_1.3.2           
##  [79] splines_4.4.1          lattice_0.22-6         survival_3.7-0        
##  [82] deldir_2.0-4           tidyselect_1.2.1       miniUI_0.1.1.1        
##  [85] pbapply_1.7-2          knitr_1.46             gridExtra_2.3         
##  [88] scattermore_1.2        RhpcBLASctl_0.23-42    xfun_0.44             
##  [91] matrixStats_1.3.0      pheatmap_1.0.12        stringi_1.8.4         
##  [94] lazyeval_0.2.2         yaml_2.3.8             evaluate_0.23         
##  [97] codetools_0.2-20       cli_3.6.3              uwot_0.2.2            
## [100] xtable_1.8-4           reticulate_1.37.0      munsell_0.5.1         
## [103] jquerylib_0.1.4        globals_0.16.3         spatstat.random_3.2-3 
## [106] png_0.1-8              parallel_4.4.1         ellipsis_0.3.2        
## [109] dotCall64_1.1-1        listenv_0.9.1          viridisLite_0.4.2     
## [112] scales_1.3.0           ggridges_0.5.6         leiden_0.4.3.1        
## [115] rlang_1.1.4
---
title: "Integrate spatial data"
date: "`r format(Sys.time(), '%d %B, %Y')`"
format:
  html:
    embed-resources: true
    code-fold: show
params:
  fig.path: "`r paste0(params$fig.path)`" #./Figures/
editor_options: 
  chunk_output_type: console
---

```{r background-job, eval=FALSE, include=FALSE}
source("../bin/render_with_jobs.R")

# quarto
# render_html_with_job(out_dir = lab_dir)
# fs::file_move(path = file, new_path = paste0(lab_dir, file))

# currently using quarto for github and kniter for html du to source code option 
render_git_with_job(fig_path = "./Figures/02/")
# Change the figure path from ./Figures/03/ to ../Figures/03/:
system2(command = "sed", stdout = TRUE,
        args = c("-i", "''","-e", 's/src=\\"\\./src=\\"\\.\\./g',
                 paste0("./md_files/", basename("./02_integrate_st_data.md"))))

# kniter
knit_html_with_job(out_dir = "../lab_book/02_integrate_st_data/", fig_path = "./Figures/02/")
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  fig.width = 6.6929133858,
  fig.path    = params$fig.path,
  dev         = c("png"),
  dpi         = 300,
  fig.align   = "center",
  message     = FALSE,
  warning     = FALSE,
  fig.process = function(filename){
      new_filename <- stringr::str_remove(string = filename, 
                                        pattern = "-1")
      fs::file_move(path = filename, new_path = new_filename)
      ifelse(fs::file_exists(new_filename), new_filename, filename)})

# setwd("~/work/Brolidens_work/Projects/Spatial_Microbiota/src")
```

### Load data and libraries
```{r Load-Library-and-data}
##################
# LOAD LIBRARIES #
##################
library(tidyverse)
library(Seurat)
library(tidyseurat)
library(cowplot)
library(harmony)
source("../bin/plotting_functions.R")

#########
# PATHS #
#########
input_dir <- "../results/01_QC_st_data/"
result_dir <- "../results/02_integrate_st_data/"
if( isFALSE(dir.exists(result_dir)) ) { dir.create(result_dir,recursive = TRUE) }

#############
# LODA DATA #
#############
DATA <- readRDS(paste0(input_dir,"seuratObj_filtered.RDS"))

```

### Identify Highly Variable Genes (HVG) across samples
```{r Find-HVG}
################################
# SPLIT INTO SEPERATE DATASETS #
################################
DATA_nested <- DATA %>%
  mutate(batch = orig.ident) %>%
  nest(data = -batch) %>%
  mutate(data = imap(
    data, ~ .x %>%
      NormalizeData(., normalization.method = "LogNormalize", 
                    verbose = FALSE) %>%
      FindVariableFeatures(selection.method = "vst", 
                           nfeatures = 2000, 
                           verbose = FALSE) )) %>%
  mutate(data = setNames(.[["data"]], .$batch))

#########################################
# FIND HIGLY VARIABLE GENES PER DATASET #
#########################################
hvgs_heat <- DATA_nested %>%
  .$data %>%
  map(., ~ .x@assays$RNA@var.features) %>%
  ( function(x){unique(unlist(x)) ->> hvgs_all; return(x)} ) %>%
  # intersect across all samples:
  ( function(x){Reduce(intersect, x) ->> hvgs; return(x)} ) %>% 
  imap_dfc(., ~hvgs_all %in% .x, .id=.y) %>%
  mutate(rownames = hvgs_all) %>%
  column_to_rownames(var = "rownames")

# choose the hvg present in at least two samples:
hig_var <- rownames(hvgs_heat)[rowSums(hvgs_heat)>2]

# remove all VDJ-genes from list of HVG
remove <- str_subset(hig_var, "^IGH|^IGK|^IGL|^TRA|^TRB|^TRD|^TRG")
hig_var <- setdiff(hig_var, remove)
```

### Heatmap of HVG in all samples
```{r 02a_HVG_heatmap, fig.width=10}
pheatmap::pheatmap(t(hvgs_heat * 1), cluster_rows = F, color = c("grey90", "grey20"))
```


### Integration
```{r Integration}
############
# HARMONY #
###########
DATA <- DATA %>%
  SCTransform(verbose = FALSE) %>%
  FindVariableFeatures(selection.method = "vst",
                      nfeatures = 4000,
                      verbose = FALSE) %>%
  ScaleData(verbose = FALSE, features = hig_var ) %>%
  RunPCA(verbose = FALSE, npcs = 50) %>%
  RunUMAP(dims = 1:50,
          n.components = 2L,
          n.neighbors = 10,
          min.dist = .1,
          spread = .3) 

DATA <- DATA %>%
  RunHarmony(group.by.vars = "orig.ident", 
             reduction.use = "pca",
             dims.use = 1:50, 
             assay.use = "RNA") #%>%

DATA <-   DATA %>%
  RunUMAP(dims = 1:50, 
          n.neighbors = 10,
          min.dist = .1,
          spread = 1,
          repulsion.strength = 1,
          negative.sample.rate = 10,
          n.epochs = 100,
          reduction = "harmony",
          reduction.name = "umapharmony")
```

### Alternative graph based UMAP
```{r alternative-way-of-UMAP}
integrated <- DATA@reductions$harmony@cell.embeddings
ann <- RcppHNSW::hnsw_build(as.matrix(integrated), distance = "cosine")
knn <- RcppHNSW::hnsw_search(as.matrix(integrated) , ann = ann , k = 15)

UU2 <- uwot::umap(X = NULL,
                 nn_method =  knn,
                 n_components = 2,
                 ret_extra = c("model","fgraph"),
                 verbose = T,
                 min_dist = 0.1,
                 spread = .3,
                 repulsion_strength = 1,
                 negative_sample_rate = 10,
                 n_epochs = 150,
                 n_threads = 8)
dimnames(UU2$embedding) <- list(colnames(DATA),paste0("umap_harmony_knn_", 1:2))
DATA@reductions[["umap_harmony_knn"]] <- CreateDimReducObject(embeddings = UU2$embedding, 
                                                              key = "umap_harmony_knn_")
colnames(DATA@reductions$umap_harmony_knn@cell.embeddings) <- paste0("umap_harmony_knn_", 1:2)
```

```{r, 02b_UMAP_options, fig.asp=5/10}
res <- c("umapharmony", "umap_harmony_knn")
p <- map(res, ~plot_clusters.fun(DATA, red=.x, cluster="orig.ident", lable=FALSE, txt_size = 7))
plot_grid(ncol = 2, 
          plotlist = p)
```

### Plot before and after integration
```{r 02c_Plot_dim_reduction, fig.height=6}
#  dev.new(height=6, width=6.6929133858, noRStudioGD = TRUE)
res <- c("PC", "harmony", "UMAP", "umapharmony")
title <- c("PCA raw data", "PCA Harmony integrated", "UMAP raw data", "UMAP Harmony integrated")
p <- map2(res, title, 
          ~plot_clusters.fun(DATA, 
                             cluster="orig.ident", txt_size = 9,
                             red=.x, lable=FALSE, title=.y))
plot_grid(ncol = 2, 
         plotlist = p)
```

### Plot marker genes
```{r 02d_plot_marker_genes, fig.height=3, fig.width=8}
#  dev.new(height=3, width=8, noRStudioGD = TRUE)
################################
# VISUALIZE EXPR. OF KEY GENES #
################################
# col <- c("grey90","grey80","grey60","navy","black")
col <- c("#FFF5F0", "#FEE0D2", "#FCBBA1", "#FC9272", "#FB6A4A", "#EF3B2C", "#CB181D", "#A50F15", "#67000D")
genes <- c("KRT1", "KRT15", "CDH1")
# genes <- c("CD8A", "SFRP2", "CD3E")
# genes <- c("CD8A", "MYOZ2", "CD3E", "EPCAM", "COL6A1", "CD4")

p <- map(genes, ~plot_genes.fun(DATA, .x, col = col, lable = FALSE, red="umapharmony"))
plot_grid(ncol = 3, 
          plotlist = p)
```

## Save seurat object
```{r save-SeuratObj}
##################################
# SAVE INTERMEDIATE SEURAT OJECT #
##################################
saveRDS(DATA, paste0(result_dir,"seuratObj_integrated_SCTnorm.RDS"))
# DATA <- readRDS(paste0(result_dir,"seuratObj_integrated.RDS"))
```

### Session info
```{r}
sessionInfo()
```
